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Automated mapping of Java code on SIMD and GPU accelerators

Abstract

Advantageous architectural features are severely under-utilized in today's computing environments. This is especially apparent within interpreted language deployments which are widely used in cloud computing and datacenter infrastructures. Interpreted languages, like Java, require a virtual machine to make decisions on how to run a program given available hardware. These mappings and optimizations are currently limited with very constrained hardware support.

This project involves the automated mapping of vectorizable code to either the internal SIMD unit or the GPU based on architecture snapshot and scheduling algorithm. The Jikes RVM JIT is being extended to detect vectorizable code segments and compile them for the internal SIMD unit and GPU. When the hotspot is detected again, the code is offloaded to either the GPU or SIMD unit depending on current utilization.